Computer users have access to many sources of information, including social networks and news media. In addition to the social aspects, these information sources can often be used to provide valuable data that allows an individual or business/organization employee to drive business decisions.
Given the vast amount of data that are available and accessible from current data sources, aggregation tools are often needed to allow the user to adequately comprehend the information. There are many types of information aggregation solutions that have been used to help automate the data gathering/comprehension process. Examples of such tools include RSS readers, portals, and mash-up interfaces.
The problem with these existing aggregation tools is that, while useful to acquire data from public information sources, these tools do not have the capability to effectively or efficiently access and/or integrate information from enterprise application systems and/or data sources, which data may be a cross-section of a corpus of enterprise data.
For example, consider the typical RSS reader. RSS readers are a type of data aggregation tool which is used to pull news and social media from different sources together. The problem is that RSS readers cannot effectively relate topics to each other or to subscribers, because by their nature, RSS streams are independent.
Portals aggregate analytic and textual information by providing a single place where aggregated content is displayed in small regions called “portlets” side by side. However, known portals do not automatically relate information in different portlets to each other or the user.
Legacy implementations of mash-ups fail to relate and present information selected from sources involving both structured data and related unstructured data.
As is evident, conventional tools are unable to integrate with enterprise data, and are unable to effectively relate the different acquired data sets to each other or to a given user. Without being able to handle these functions, the tools cannot optimally help users to correlate against the acquired data. This creates barriers to the user being able to effectively obtain, identify, and relate important topics, and can therefore frustrate the ultimate goal of allowing the user to comprehend the state of their business and drive sound business decisions.
None of the aforementioned legacy approaches achieve the capabilities of the herein-disclosed techniques for relating enterprise information with public information based on a schema and user profile. Therefore, there is a need for improvements.